A WiFi-Based Passive Fall Detection System

Fall detection systems based on WiFi signals are gaining popularity recently. However, most of the existing works relying on training are environment-dependent. In this paper, we propose DeFall, a novel WiFi-based environment-independent fall detection system by leveraging the features inherently associated with human falls - the patterns of speed and acceleration over time. The system consists of an offline template-generating stage and an online decision-making stage. In the offline stage, the speed of human falls is first estimated based on a statistical modeling about the Channel State Information (CSI). Dynamic Time Warping (DTW) based algorithms are applied to generate a representative template for typical human falls. Then fall event is detected in the online stage by evaluating the similarity between the patterns of realtime speed/acceleration estimates and the representative template. Extensive experiment results show that with a single pair of WiFi transceivers, the proposed system can achieve a detection rate of 96% and a false alarm rate smaller than 1.5% under both line-of-sight (LOS) and non-LOS (NLOS) scenarios.